| Signal Processing Toolbox | |
Compute an estimate of AR model parameters using the Burg method.
Syntax
a=arburg(x,p) [a,e]=arburg(x,p) [a,e,k]=arburg(x,p)
Description
a = arburg(x,p)
uses the Burg method to fit a pth order autoregressive (AR) model to the input signal, x, by minimizing (least squares) the forward and backward prediction errors while constraining the AR parameters to satisfy the Levinson-Durbin recursion. x is assumed to be the output of an AR system driven by white noise. Vector a contains the normalized estimate of the AR system parameters, A(z), in descending powers of z.
Since the method characterizes the input data using an all-pole model, the correct choice of the model order p is important.
[a,e] = arburg(x,p)
returns the variance estimate, e, of the white noise input to the AR model.
[a,e,k] = arburg(x,p)
returns a vector, k, of reflection coefficients.
See Also
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Compute an estimate of AR model parameters using the covariance method. |
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Compute an estimate of AR model parameters using the modified covariance method. |
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Compute an estimate of AR model parameters using the Yule-Walker method. |
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Compute linear predictive recursion coefficients. |
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Compute the power spectrum estimate using the Burg method. |
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Prony's method for time-domain IIR filter design. |
| | angle | arcov | |